Fundus image analysis presents several challenges, such as high image variability, the need for reliable processing in the face of nonideal imaging conditions and short computation deadlines. Large variability is observed between different patients—even if healthy, with the situation worsening when pathologies exist. For the same patient, variability is observed under differing imaging conditions and during the course of a treatment or simply a long period of time. Besides, fundus images are often characterized by having a limited quality, being subject to improper illumination, glare, fadeout, loss of focus and artifacts arising from reflection, refraction, and dispersion.
Automatic extraction and analyzation of the vascular tree of fundus images is an important task in fundus image analysis for several reasons. First of all the vascular tree is the most prominent feature of the retina, and it is present regardless of health condition. This makes the vascular tree an obvious basis for automated registration and montage synthesis algorithms. Besides, the task of automatic and robust localization of the optic nerve head and fovea, as well as the task of automatic classification of veins and arteries in fundus images may very well rely on a proper extraction of the vascular tree. Another example is the task of automatically detecting lesions which in many cases resemble the blood vessels. A properly extracted vessel tree may be a valuable tool in disqualifying false positive responses produced by such an algorithm, thus increasing its specificity. Finally the vessels often display various pathological manifestasions themselves, such as increased tortuosity, abnormal caliber changes and deproliferation. An automatic vessel tracking algorithm would be the obvious basis for analysis of these phenomena as well. Diabetes is the leading cause of blindness in working age adults. It is a disease that, among its many symptoms, includes a progressive impairment of the peripheral vascular system. These changes in the vasculature of the retina cause progressive vision impairment and eventually complete loss of sight. The tragedy of diabetic retinopathy is that in the vast majority of cases, blindness is preventable by early diagnosis and treatment, but screening programs that could provide early detection are not widespread.
Promising techniques for early detection of diabetic retinopathy presently exist. Researchers have found that retinopathy is preceded by visibly detectable changes in blood flow through the retina. Diagnostic techniques now exist that grade and classify diabetic retinopathy, and together with a series of retinal images taken at different times, these provide a methodology for the early detection of degeneration. Various medical, surgical and dietary interventions may then prevent the disease from progressing to blindness.
Despite the existing techniques for preventing diabetic blindness, only a small fraction of the afflicted population receives timely and proper care, and significant barriers separate most patients from state-of-the art diabetes eye care. There are a limited number of ophthalmologists trained to evaluate retinopathy, and most are located in population centers. Many patients cannot afford the costs or the time for travel to a specialist. Additionally, cultural and language barriers often prevent elderly, rural and ethnic minority patients from seeking proper care. Moreover, because diabetes is a persistent disease and diabetic retinopathy is a degenerative disease, an afflicted patient requires lifelong disease management, including periodic examinations to monitor and record the condition of the retina, and sustained attention on the part of the patient to medical or behavioral guidelines. Such a sustained level of personal responsibility requires a high degree of motivation, and lifelong disease management can be a significant lifestyle burden. These factors increase the likelihood that the patient will, at least at some point, fail to receive proper disease management, often with catastrophic consequences.
Accordingly, it would be desirable to implement more widespread screening for retinal degeneration or pathology, and to positively address the financial, social and cultural barriers to implementation of such screening. It would also be desirable to improve the efficiency and quality of retinal evaluation.
Hence, a precise knowledge of both localisation and orientations of the strucutures of the fundus is important, including the localisation of the vessels. Currently, examination of fundus images is carried out principally by a clinician examining each image “manually”. This is not only very time-consuming, since even an experienced clinician can take several minutes to assess a single image, but is also prone to error since there can be inconsistencies between the way in which different clinicians assess a given image. It is therefore desirable to provide ways of automating the process of the analysis of fundus images, using computerised image analysis, so as to provide at least preliminary screening information and also as an aid to diagnosis to assist the clinician in the analysis of difficult cases.
Next, it is generally desirable to provide a method of accurately determining, using computerised image analysis techniques, the position of both the papilla (the point of exit of the optic nerve) and the fovea (the region at the centre of the retina, where the retina is most sensitive to light), as well as vessels of the fundus.